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Apache lucene data lineage6/18/2023 Select any service link to see configuration and service information. For example, you may see YARN Queue Manager, Hive View, and Tez View. This list varies, depending on which libraries you've installed. To open a list of service views, select the Ambari Views pane on the Azure portal page for HDInsight. You're prompted for your cluster login credentials. Next, select the HDInsight cluster dashboard pane to open the Ambari UI. Select the Cluster Dashboard pane on the Azure portal HDInsight page to open the Cluster Dashboards link page. Ambari is included on Linux-based HDInsight clusters. View cluster configuration settings with the Ambari UIĪpache Ambari simplifies the management, configuration, and monitoring of a HDInsight cluster by providing a web UI and a REST API. For some workloads, such as bioinformatics, you may be required to retain service configuration log history in addition to job execution logs. Step 2: Manage cluster service versions and view logsĪ typical HDInsight cluster uses several services and open-source software packages (such as Apache HBase, Apache Spark, and so forth). Many companies offer services to monitor Hadoop-based big data solutions, for example: Centerity, Compuware APM, Sematext SPM, and Zettaset Orchestrator. You can also use third-party tools such as Apache Chukwa and Ganglia to collect and centralize logs. The Microsoft System Center provides an HDInsight management pack. You can build these utilities using PowerShell, the HDInsight SDKs, or code that accesses the Azure classic deployment model.Ĭonsider whether a monitoring solution or service would be a useful benefit. You can also add other capabilities for alerting for security or failure detection. You might use a custom solution to access and download the log files regularly, and combine and analyze them to provide a dashboard display. This allows you to trace back the original source of the data and the operation, and follow the data through each stage to understand its consistency and validity.Ĭonsider how you can collect logs from the cluster, or from more than one cluster, and collate them for purposes such as auditing, monitoring, planning, and alerting. Do any of the workloads have associated regulatory execution lineage requirements?Įxample log retention patterns and practicesĬonsider maintaining data lineage tracking by adding an identifier to each log entry, or through other techniques.Do any of the workloads use a complex set of Hadoop services for which multiple types of logs are produced?.Are any of the workloads resource-intensive and/or long-running?.How often do the production-quality workloads normally run?.Are the workloads experimental (such as development or test) or production-quality?.It's important to understand the workload types running on your HDInsight cluster(s) to design appropriate logging strategies for each type. Understand the workloads running on your clusters For more information, see Apache Manage Hadoop clusters in HDInsight by using Azure PowerShell. You can also use PowerShell to view this information. Alternatively, you can use Azure CLI to get information about your HDInsight cluster(s): az hdinsight list -resource-group Īz hdinsight show -resource-group -name You can get most of this top-level information using the Azure portal. Type and number of HDInsight instances specified for the master, core, and task nodes.Cluster state, including details of the last state change.Cluster region and Azure availability zone.Gather this information from all HDInsight clusters you've created in a particular Azure account. The following cluster details are useful in helping to gather information in your log management strategy. The first step in creating a HDInsight cluster log management strategy is to gather information about business scenarios and job execution history storage requirements. Step 5: Determine log archive policies and processes.Step 4: Forecast log volume storage sizes and costs.Step 3: Manage cluster job execution log files.Step 2: Manage cluster service versions configuration logs.Step 1: Determine log retention policies.Typical steps in HDInsight log management are: This information includes all associated Azure Service logs, cluster configuration, job execution information, any error states, and other data as needed. Managing HDInsight cluster logs includes retaining information about all aspects of the cluster environment. Due to the number and size of log files, optimizing log storage and archiving helps with service cost management. There can also be regulatory requirements for log archiving. Log file management is part of maintaining a healthy HDInsight cluster. For example, Apache Hadoop and related services, such as Apache Spark, produce detailed job execution logs. An HDInsight cluster produces variois log files.
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